Designing, validation and evaluation of the expert system of "Healthy Menopause" and assessing its effect on the management of menopause symptoms: an exploratory mixed method study protocol.

BACKGROUND: Menopause is a period of women's life that has the especial physical, psychological and social challenges. So provision of an effective, practical and affordable way for meeting women's related needs is important. In addition, women should be able to incorporate such programs into their daily work. Considering the dearth of suitable services in this regard, this study will be conducted with the aim of designing, validating and evaluating the "Healthy Menopause" expert system on the management of menopausal symptoms. METHODS/DESIGN: A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase is a qualitative conventional content analysis study with purposes of exploring the women's experience of menopausal symptoms and extracting their needs, and collecting data about their expectations from a healthy menopause expert system.. The purposive sampling (In his phase data will be gathered through interviewing menopaused women aged 40 to 60 years old and other persons that have rich information in this regard and will be continued until data saturation. The second phase includes designing a healthy menopause expert system in this stage, the needs will be extracted from the qualitative findings along with a comprehensive literature review. The extracted needs will be again confirmed by the participants. Then, through a participatory approach (Participatory Design) using nominal group or Delphi technique the experts' opinion about the priority needs of menopaused women and related solutions will be explored based on the categories of identified needs. Such findings will be used to design a healthy menopause expert system at this stage. The third phase of study is a quantitative research in which the evaluation of the healthy menopause expert system will be done through a randomized controlled clinical trial with the aim of determining the effect of the healthy menopause expert system on the management of menopause symptoms by menopausal women themselves. DISCUSSION: This is the first study that uses a mixed method approach for designing, validating and evaluating of the expert system "Healthy Menopause". This study will fill the research gap in the field of improving menopausal symptoms and designing a healthy menopause expert system based on the needs of the large group of menopause women. We hope that by applying this expert system, the menopausal women be empowered to management and improving their health with an easy and affordable manner.

Menopause is a period of women's life that has the especial physical, psychological and social challenges. So provision of an effective, easy for use and affordable way for managing related problems and meeting related needs is important. Menopause is a period of women's life that has physical, psychological and social consequences. It is important to identify methods that are effective, practical and affordable. New technologies can increase women's ability to access educational information. This is the first study for designing, validating and evaluating of the expert system "Healthy Menopause". A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase (qualitative): The conventional content analysis method will be used. The second phase: Designing a healthy menopause expert system: It is based on the codes of women's challenges from the first phase, along with conducting interviews and literature review. The participatory approach (Participatory Design) through nominal group or if needed, Delphi method based on the categories of needs and solutions by considering the opinions of the participants, available experts related to this issue will be listed. It should be used to design a healthy menopause expert system at this stage. The third phase (quantitative): The evaluation of the healthy menopause expert system will be a randomized clinical trial that determine the effect of the healthy menopause expert system on the management of menopause symptoms. In the present study an expert system (ES) will be designed that can be installed on mobile phones and computers. This tool is not only educational but also interactively helps to adapt to continuous changes, so by asking questions about menopause the system will respond as if an expert (midwife or gynecologist) is giving advice.

Artificial Intelligence Sensing: Effective Flavor Blueprinting of Tea Infusions for a Quality Control Perspective.

Tea infusions are the most consumed beverages in the world after water; their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for the industry. Any qualification method capable of objectifying the product's sensory features effectively supports industrial quality control laboratories in guaranteeing high sample throughputs even without human panel intervention. The current study presents an integrated analytical strategy acting as an Artificial Intelligence decision tool for black tea infusion aroma and taste blueprinting. Key markers validated by sensomics are accurately quantified in a wide dynamic range of concentrations. Thirteen key aromas are quantitatively assessed by standard addition with in-solution solid-phase microextraction sampling followed by GC-MS. On the other hand, nineteen key taste and quality markers are quantified by external standard calibration and LC-UV/DAD. The large dynamic range of concentration for sensory markers is reflected in the selection of seven high-quality teas from different geographical areas (Ceylon, Darjeeling Testa Valley and Castleton, Assam, Yunnan, Azores, and Kenya). The strategy as a sensomics-based expert system predicts teas' sensory features and acts as an AI smelling and taste machine suitable for quality controls.

Application of the SeDeM system for the preparation of antiparasitic tablets from mesquite flour for use in sheep.

The use of herbal medicine to treat various diseases is becoming increasingly important as an alternative therapy. Numerous plants have been traditionally used for different purposes, including antiparasitic in humans and animals. Diseases caused by gastrointestinal parasites in ruminants, especially by the nematode Haemonchus contortus, cause large economic losses to the producers, whether by complications of the diseases or the cost of treatment. The main way of handling nematodiasis is by administering anthelmintic drugs, but their excessive use has the disadvantage of causing drug resistance; therefore, an alternative is the use of herbal medicine for this purpose. Mesquite (Prosopis spp.) has been used in Mexico to treat gastrointestinal diseases attributed to helminths. The present study aimed to characterize the rheological properties of mesquite flour using the SeDeM Expert System to determine its suitability for tablet production by direct compression. Direct compression technology facilitates the tableting process by reducing manufacturing costs. The results of the present study indicate that mesquite flour can be processed by direct compression. The latter could allow the manufacturing of economic tablets to treat infections by H. contortus in ruminants.

Computer-aided diagnosis of primary membranous nephropathy using expert system.

BACKGROUND: The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue. METHODS: This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses. RESULTS: 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy. CONCLUSIONS: Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy.

Improvements to in silico skin sensitisation predictions through privacy-preserving data sharing.

In silico models are often built solely on publicly available data which may mean that they are less predictive for proprietary chemical space. Data sharing initiatives can improve the performance of such models, but organisations are often unable to share their data due to the need to protect their business interests and maintain the confidentiality of the chemicals in their research and development programmes. In silico models like Derek Nexus, which use expert knowledge to develop structural alerts based on chemical toxicity, can use proprietary data to identify new areas of chemical space and/or refine existing alerts whilst still preserving the privacy of the confidential data. Five hundred and thirty seven proprietary chemicals with skin sensitisation data were shared which led to the implementation of 7 new alerts and 5 modified alerts, with a concomitant 19% increase in sensitivity and 3% increase in specificity of the model.

Therapy-based expert system on function and postural stability after anterior cruciate ligament reconstruction: a pilot study.

PURPOSE: Wii Fit exergames have been less commonly used for the rehabilitation of athletes after Anterior Cruciate Ligament Reconstruction (ACLR). This study aims to investigate the effects of an expert system using Wii Fit exergames compared to conventional rehabilitation following ACLR. A forward-chaining rule-based expert system was developed which proposed a rehabilitation program that included the number and type of exercise in terms of difficulty and ease and the duration of each exercise in a progressive manner according to the patient's physical condition. MATERIALS AND METHODS: Twenty eligible athletes aged 20-30 who underwent ACLR were enrolled in this study and randomly assigned to two groups; and received 12 sessions of either Wii Fit exergames as Wii group (n = 10) or conventional rehabilitation as CL group (n = 10). RESULTS: The main outcomes consisted of pain (Visual Analogue Scale (VAS)), knee effusion, knee flexion range (KFR), thigh girth (TG), single-leg hop for distance (SLHD), and for time (SLHT), static and dynamic balance tests. Both groups had considerable improvement in all outcomes, also there were significantly differences between Wii and CL groups as follows; VAS (P < 0.001), knee effusion (P < 0.001), TG (P = 0.001), KFR (P = 0.012), static balance in stable position (P < 0.001) and in unstable position (P = 0.001), dynamic balance in the anterior (P < 0.001), posteromedial (P < 0.001), posterolateral (P = 0.004) directions, symmetry index of SLHD (P < 0.001) and symmetry index of SLHT (P = 0.013). CONCLUSIONS: The findings showed that using Wii Fit exergames in post-ACLR patients reduced pain and effusion while also improving function and balance significantly. Iranian Registry of Clinical Trials registration number is IRCT20191013045090N1, and the registration date is 03-03-2020.

The Hombres Saludables Physical Activity Web-Based and Mobile Phone Intervention: Pilot Randomized Controlled Trial With Latino Men.

BACKGROUND: Owing to structural-level, interpersonal-level, and individual-level barriers, Latino men have disproportionately high rates of physical inactivity and experience related chronic diseases. Despite these disparities, few physical activity (PA) interventions are culturally targeted for Latino men. OBJECTIVE: This study reported the feasibility and acceptability of Hombres Saludables PA intervention for Latino men. We also reported the preliminary efficacy of the intervention on PA change and provided the results of the exploratory moderator and mediator analysis. METHODS: We completed a 6-month, single-blind, pilot randomized controlled trial of Hombres Saludables with Latino men aged between 18 and 65 years. Men were randomized to either (1) a theory-driven, individually tailored, internet-based and SMS text message-based, Spanish-language PA intervention arm or (2) a nutrition and wellness attention contact control arm that was also delivered via the web and SMS text message. We assessed the primary study outcomes of feasibility using participant retention and acceptability using postintervention survey and open-ended interview questions. We measured the preliminary efficacy via change in minutes of moderate to vigorous PA per week using ActiGraph wGT3X-BT accelerometry (primary measure) and self-reported minutes per week using 7-day Physical Activity Recall. Participants completed the assessments at study enrollment and after 6 months. RESULTS: The 38 participants were predominantly Dominican (n=8, 21%) or Guatemalan (n=5, 13%), and the mean age was 38.6 (SD 12.43) years. Retention rates were 91% (21/23) for the PA intervention arm and 100% (15/15) for the control arm. Overall, 95% (19/20) of the intervention arm participants reported that the Hombres study was somewhat to very helpful in getting them to be more physically active. Accelerometry results indicated that participants in the intervention group increased their PA from a median of 13 minutes per week at study enrollment to 34 minutes per week at 6 months, whereas the control group participants showed no increases. On the basis of self-reports, the intervention group was more likely to meet the US PA guidelines of 150 minutes per week of moderate to vigorous PA at 6-month follow-up, with 42% (8/19) of the intervention participants meeting the PA guidelines versus 27% (4/15) of the control participants (odds ratio 3.22, 95% CI 0.95-13.69). Exploratory analyses suggested conditional effects on PA outcomes based on baseline stage of motivational readiness, employment, and neighborhood safety. CONCLUSIONS: The PA intervention demonstrated feasibility and acceptability. Results of this pilot study indicate that the Hombres Saludables intervention is promising for increasing PA in Latino men and suggest that a fully powered trial is warranted. Our technology-based PA intervention provides a potentially scalable approach that can improve health in a population that is disproportionately affected by low PA and related chronic disease. TRIAL REGISTRATION: ClinicalTrials.gov NCT03196570; https://classic.clinicaltrials.gov/ct2/show/NCT03196570. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/23690.

Design and evaluation of a Mobile-Based decision support system to enhance lung transplant candidate assessment and management: knowledge translation integrated with clinical workflow.

BACKGROUND: Accurate and timely decision-making in lung transplantation (LTx) programs is critical. The main objective of this study was to develop a mobile-based evidence-based clinical decision support system (CDSS) to enhance the management of lung transplant candidates. METHOD: An iterative participatory software development process was employed to develop the ImamLTx CDSS. This study was accomplished in three phases. First, required data and standard clinical workflow were identified according to the literature review and expert consensus. Second, a rule-based knowledge-based CDSS application was developed. In the third phase, this CDSS was evaluated. The evaluation was done using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and ten usability heuristics factors for user interface design. RESULTS: According to expert consensus, fifty-five data items were identified as essential data sets using the Content Validity Ratio (CVR) formula. By integrating information flow in clinical practices with clinical protocols, more than 450 rules and 500 knowledge statements were extracted. This CDSS provides clinical decision support on an Android platform regarding inclusion and exclusion referral criteria, optimum transplant time based on the type of lung disease, findings of initial assessment, and the overall evaluation of lung transplant candidates. Evaluation results showed high usability ratings due to the fact provided accuracy and sensitivity of this lung transplant CDSS with the information quality domain receiving the highest score (6.305 from 7). CONCLUSION: Through a stepwise approach, the ImamLTx CDSS was developed to provide LTx programs with timely patient data access via a mobile platform. Our results suggest integration with existing workflow to support clinical decision-making and provide patient-specific recommendations.

Proposal of neural network model for neurocognitive rehabilitation and its comparison with fuzzy expert system model.

This article focuses on the development of algorithms for a smart neurorehabilitation system, whose core is made up of artificial neural networks. The authors of the article have proposed a completely unique transfer of ACE-R results to the CHC model. This unique approach allows for the saturation of the CHC model domains according to modified ACE-R factor analysis. The outputs of the proposed algorithm thus enable the automatic creation of a personalized and optimized neurorehabilitation plan for individual patients to train their cognitive functions. A set of tasks in 6 levels of difficulty (level 1 to level 6) was designed for each of the nine CHC model domains. For each patient, the results of the ACE-R screening helped deter-mine the specific CHC domains to be rehabilitated, as well as the initial gaming level for rehabilitation in each domain. The proposed artificial neural network algorithm was adapted to real data from 703 patients. Experimental outputs were compared to the outputs of the initially designed fuzzy expert system, which was trained on the same real data, and all outputs from both systems were statistically evaluated against expert conclusions that were available. It is evident from the conducted experimental study that the smart neurorehabilitation system using artificial neural networks achieved significantly better results than the neurorehabilitation system whose core is a fuzzy expert system. Both algorithms are implemented into a comprehensive neurorehabilitation portal (Eddie), which was supported by a research project from the Technology Agency of the Czech Republic.

Decision support system to classify the vulnerability of broiler production system to heat stress based on fuzzy logic.

In this study, we develop an artificial intelligence model to predict the vulnerability of broiler production systems (broilers and facilities) to heat conditions using a fuzzy model approach. The model was designed with a multiple-input and a single-output (MISO) approach (input: physical environment and broilers age; output: degree of vulnerability of broilers system). For the validation of the fuzzy model, two approaches were used: (1) records from the scientific literature and (2) meteorological forecasts. First, we validated the model fuzzy with data from the scientific literature; second, we validate the model with data from meteorological forecasts. Both validation approaches were performed in different scenarios of the thermal environment (comfort, discomfort, and discomfort + low heat exchange), broilers' age (21-35 days, 25-39 days, and 28-42 days), and relative cooling efficiency (0% inefficient; and 80% efficient). Then, we applied the model to predict the degree of vulnerability of the broiler system with the help of weather forecasts. The recall and precision of the fuzzy model were high (> 0.9) for the thermal comfort and thermal discomfort + low heat exchange scenarios. In contrast, the fuzzy model was moderate agreement (recall 0.45; precision 0.64) for the thermal discomfort scenario compared to the scientific literature. The application of the model with the weather forecast showed the interaction between the physical and biological systems when submitted to a thermal environment challenge. Regardless of the broilers' age, a high degree of vulnerability was observed in facilities with inefficient cooling system. The fuzzy model developed in this study was efficient to predict the vulnerability of the broiler production system to heat conditions, further, to identify the uncertain conditions associated with broilers' age, relative humidity, and the relative cooling efficiency of the facilities.

A Sustainability-Based Expert System for Additive Manufacturing and CNC Machining.

The objective of this research study is to develop a set of expert systems that can aid metal manufacturing facilities in selecting binder jetting, direct metal laser sintering, or CNC machining based on viable products, processes, system parameters, and inherent sustainability aspects. For the purposes of this study, cost-effectiveness, energy, and auxiliary material usage efficiency were considered the key indicators of manufacturing process sustainability. The expert systems were developed using the knowledge automation software Exsys Corvid®V6.1.3. The programs were verified by analyzing and comparing the sustainability impacts of binder jetting and CNC machining during the fabrication of a stainless steel 316L component. According to the results of this study, binder jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC machining, considering the fabrication of components feasible for each technology.

Predicting Injuries in Football Based on Data Collected from GPS-Based Wearable Sensors.

The growing intensity and frequency of matches in professional football leagues are related to the increasing physical player load. An incorrect training model results in over- or undertraining, which is related to a raised probability of an injury. This research focuses on predicting non-contact lower body injuries coming from over- or undertraining. The purpose of this analysis was to create decision-making models based on data collected during both training and match, which will enable the preparation of a tool to model the load and report the increased risk of injury for a given player in the upcoming microcycle. For this purpose, three decision-making methods were implemented. Rule-based and fuzzy rule-based methods were prepared based on expert understanding. As a machine learning baseline XGBoost algorithm was considered. Taking into account the dataset used containing parameters related to the external load of the player, it is possible to predict the risk of injury with a certain precision, depending on the method used. The most promising results were achieved by the machine learning method XGBoost algorithm (Precision 92.4%, Recall 96.5%, and F1-score 94.4%).

Development of Child-Friendly Lisdexamfetamine Chewable Tablets Using Ion Exchange Resin as a Taste-Masking Carrier Based on the Concept of Quality by Design (QbD).

Taste masking is critical to improving the compliance of pediatric oral dosage forms. However, it is challenging for extremely bitter lisdexamfetamine dimesylate (LDX) with a long half-life and given in large dose. The present study aims to develop an immediate-release, taste-masked lisdexamfetamine chewable tablet. Lisdexamfetamine-resin complexes (LRCs) were prepared using the batch method. The molecular mechanism of taste masking was explored by PXRD, PLM, STA, and FT-IR. The results showed that taste masking was attributed to the ionic interaction between drug and the resin. The ion exchange process conformed to first-order kinetics. The rate-limiting step of drug release was the diffusion of ions inside the particles, and the concentration of H+ was the key factor for immediate release. The masking efficiency of the prepared LRCs in saliva exceeded 96%, and the drug could be completely released within 15 min in aqueous HCl (pH 1.2). Furthermore, the SeDeM expert system was used for the first time to comprehensively study the powder properties of LRCs and to quickly visualize their defects (compressibility, lubricity/stability, and lubricity/dosage). The selection of excipients was targeted rather than traditional screening, thus obtaining a robust chewable tablet formulation suitable for direct compression. Finally, the difference between chewable tablets containing LRCs and chewable tablets containing lisdexamfetamine dimesylate was compared by in vitro dissolution test, electronic tongue, and disintegration test. In conclusion, an immediate-released, child-friendly lisdexamfetamine chewable tablets without bitterness was successfully developed by the QbD approach, using the SeDeM system, which may help in further development of chewable tablets.

Evaluation of the VITEK2 AST-XN17 card for the detection of carbapenemase-producing Enterobacterales in isolates primarily producing metallo ß-lactamase.

Carbapenemase-producing Enterobacterales (CPE) are not always resistant to carbapenem antimicrobial susceptibility testing (AST) and can be difficult to detect. With the newly created VITEK2 AST-XN17 card, the types of antibiotics measured in AST can be increased. In this study, we evaluated the detectability of CPE using the results of AST with multiple antimicrobial agents with additional measurements of the AST-XN17 card. In addition, we evaluated the CPE detectability of comments on CPE using the VITEK2 Advance Expert System (AES). In total, 169 Enterobacterales samples, including 76 non-CPE and 93 CPE, collected from multiple medical institutions in the Kinki region of Japan, were used in this investigation. AST with VITEK2 was performed by adding the AST-XN17 card in addition to the AST-N268 or AST-N404 card. Measurement results were identified using cutoff values, primarily Clinical and Laboratory Standards Institute breakpoints, and the CPE detection capability of each antibiotic was evaluated in several terms, including sensitivity and specificity. The drugs highly sensitive to CPE detection were faropenem (FRPM) > 2 µg/mL at 100% and meropenem > 0.25 µg/mL at 98.9%; the highest specificity to CPE detection was for avibactam/ceftazidime (AVI/CAZ) > 8 µg/mL at 100%. The sensitivity and specificity of each card in the AES output were 86.2% and 94.7% for AST-N404 and AST-XN17 and 91.5% and 90.8% for AST-N268 and AST-XN17, respectively. AST using the VITEK2 AST-XN17 card is a useful test method of screening for CPE.

Updating the Dermal Sensitisation Thresholds using an expanded dataset and an in silico expert system.

The Dermal Sensitisation Thresholds (DST) are Thresholds of Toxicological Concern, which can be used to justify exposure-based waiving when conducting a skin sensitisation risk assessment. This study aimed to update the published DST values by expanding the size of the Local Lymph Node Assay dataset upon which they are based, whilst assigning chemical reactivity using an in silico expert system (Derek Nexus). The potency values within the expanded dataset fitted a similar gamma distribution to that observed for the original dataset. Derek Nexus was used to classify the sensitisation activity of the 1152 chemicals in the expanded dataset and to predict which chemicals belonged to a High Potency Category (HPC). This two-step classification led to three updated thresholds: a non-reactive DST of 710 µg/cm2 (based on 79 sensitisers), a reactive (non-HPC) DST of 73 µg/cm2 (based on 331 sensitisers) and an HPC DST of 1.0 µg/cm2 (based on 146 sensitisers). Despite the dataset containing twice as many sensitisers, these values are similar to the previously published thresholds, highlighting their robustness and increasing confidence in their use. By classifying reactivity in silico the updated DSTs can be applied within a skin sensitisation risk assessment in a reproducible, scalable and accessible manner.

The Nature-Based Solutions Case-Based System: A hybrid expert system.

Deriving knowledge and learning from past experiences is essential for the successful adoption of Nature-Based Solutions (NBS) as novel integrative solutions that involve many uncertainties. Past experiences in implementing NBS have been collected in a number of repositories; however, it is a major challenge to derive knowledge from the huge amount of information provided by these repositories. This calls for information systems that can facilitate the knowledge extraction process. This paper introduces the NBS Case-Based System (NBS-CBS), an expert system that uses a hybrid architecture to derive information and recommendations from an NBS experience repository. The NBS-CBS combines a 'black-box' artificial neural networks model with a 'white-box' case-based reasoning model to deliver an intelligent, adaptive, and explainable system. Experts have tested this system to assess its functionality and accuracy. Accordingly, the NBS-CBS appears to provide inspirational recommendations and information for the NBS planning and design process.

Safety-Stock: Predicting the demand for supplies in Brazilian hospitals during the COVID-19 pandemic.

Many challenges lie ahead when dealing with COVID-19, not only related to the acceleration of the pandemic, but also to the prediction of personal protective equipment sets consumption to accommodate the explosive demand. Due to this situation of uncertainty, hospital administration encourages the excess stock of these materials, over-stocking products in some hospitals, and provoking shortages in others. The number of available personal protective equipment sets is one of the three main factors that limit the number of patients at a hospital, as well as the number of available beds and the number of professionals per shift. In this scenario, we developed an easy-to-use expert system to predict the demand for personal protective equipment sets in hospitals during the COVID-19 pandemic, which can be updated in real-time for short term planning. For this system, we propose a naive statistical modeling which combines historical data of the consumption of personal protective equipment sets by hospitals, current protocols for their uses and epidemiological data related to the disease, to build predictive models for the demand for personal protective equipment in Brazilian hospitals during the pandemic. We then embed this modeling in the free Safety-Stock system, which provides useful information for the hospital, especially the safety-stock level and the prediction of consumption/demand for each personal protective equipment set over time. Considering our predictions, a hospital may have its needs related to specific personal protective equipment sets estimated, taking into account its historical stock levels and possible scheduled purchases. The tool allows for adopting strategies to control and keep the stock at safety levels to the demand, mitigating the risk of stock-out. As a direct consequence, it also enables the interchange and cooperation between hospitals, aiming to maximize the availability of equipment during the pandemic.

[Intelligent fault diagnosis expert system for multi-parameter monitor based on fault tree].

Aiming at the dilemma of expensive and difficult maintenance, lack of technical data and insufficient maintenance force for modern medical equipment, an intelligent fault diagnosis expert system of multi-parameter monitor based on fault tree was proposed in this study. Firstly, the fault tree of multi-parameter monitor was established and analyzed qualitatively and quantitatively, then based on the analysis results of fault tree, the expert system knowledge base and inference engine were constructed and the overall framework of the system was determined, finally the intelligent fault diagnosis expert system for multi-parameter monitor was developed by using the page hypertext preprocessor (PHP) language, with an accuracy rate of 80% in fault diagnosis. The results showed that technology fusion on the basis of fault tree and expert system can effectively realize intelligent fault diagnosis of multi-parameter monitors and provide troubleshooting suggestions, which can not only provide experience accumulation for fault diagnosis of multi-parameter monitors, but also provide a new idea and technical support for fault diagnosis of medical equipment.

Evidence-Based Policymaking in Times of Acute Crisis: Comparing the Use of Scientific Knowledge in Germany, Switzerland, and Italy.

This article studies how different systems of policy advice are suited to provide relevant knowledge in times of acute crisis. The notion of evidence-based policymaking (EBP) originated in the successful 1997 New Labour program in the United Kingdom to formulate policy based not on ideology but on sound empirical evidence. We provide a brief overview of the history of the concept and the current debates around it. We then outline the main characteristics of the policy advisory systems in Germany, Switzerland, and Italy through which scientific knowledge-in the form of either person-bound expertise or evidence generated through standard scientific processes-was fed into policy formulation processes before the COVID-19 crisis. Whereas EBP takes place in the form of institutionalized advisory bodies and draws on expertise rather than on evidence in Germany, the system in Switzerland focuses more on the use of evidence provided through external mandates. Italy has a hybrid politicized expert system. The article then analyzes how this different prioritization of expertise vs. evidence in the three countries affects policymakers' capacity to include scientific knowledge in policy decisions in times of acute crisis. The comparison of the three countries implies that countries with policy advisory systems designed to use expertise are better placed to incorporate scientific knowledge into their decisions in times of acute crisis than are countries with policy advisory systems that relied primarily on evidence before the COVID-19 crisis. Supplementary Information: The online version of this article (10.1007/s11615-022-00382-x) contains supplementary material, which is available to authorized users.

Performance of the VITEK®2 advanced expert system™ for the validation of antimicrobial susceptibility testing results.

Time to reporting antimicrobial susceptibility testing (AST) results to physicians plays an essential role in antibiotic stewardship programs. Expert software has been developed for facilitating the microbiologists' AST review process. The reliability of the VITEK®2 Advanced Expert™ software to effectively alert the microbiologist in detection of atypical and inconsistent AST results was assessed at the Labor Berlin-Charité Vivantes services. The results demonstrated a confidence rate of 99.3% in reporting fully consistent AST results to physicians.